Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 2
Preface
Deep analytics does not only mean statistics or data mining or big data
analytics, it is a complex multi-dimensional analysis through ‘7-S’ model based
on rational, logical and analytical reasoning from differentperspectivessuch as
scope, system, structure, staff-resources, skill-style-support, security and strategy.
This e-book presents a deep analytics model through a consistent and systematic
approach and highlights its utility and application for reasoning seven
technology innovations today: (1) solar power electronics, (2) electrical and
hybrid vehicles, (3) Smart grid, (3) IIOT for SCADA and industrial control system,
(4) Biomedical instrumentation, (5) Cancer prevention mechanism, (6) Artificial
rainfall & cloud computing and (7) Real-time search for astronomical hazards.
The reality is that every stakeholder is impacted by the challenges and
opportunities of innovation ecosystems today. The concept of deep analytics is still
relatively new; it has now emerged as a powerful tool for business analytics and
a real world theme in the modern global economy. The target audience of this e-
book includes academic and research community, corporate leaders, policy
makers, administrators and governments, entrepreneurs, investors, engineers,
producers and directors interested in production of documentary films, news and
TV serials. I am excited to share the ideas of deep analytics with you. I hope that
you will find them really value adding and useful and will share with your
communities. It is a rational and interesting option to teach business analytics
in various academic programmes of Electrical and Electronics Engineering (e.g.
B.Tech, M.Tech, B.E.E, M.E., Ph.D.) and also business management (e.g. Technology
Management, Strategic Management and Analytics for BBA, MBA, PGDM, PGDBM).
This e-book is the online version andthe summary of the original draft, Edition 1
dated 24. 07. 2018: published by Business Analytics Research Lab, India; Price : Rs.
10,000 (per copy of online version). This e-book contains information obtained
from authentic sources; sincere efforts have been made to publish reliable data
and information. Anypart of this book may be reprinted, reproduced,
transmittedor utilized in any form by any electronic, mechanicalor other
means, now known or hereafter invented, including photocopying, microfilming
and recording or in any information storage or retrieval systemwith permission
from relevant sources.
Sumit Chakraborty
Fellow (IIMC), Bachelor of Electrical Engineering (JU),
Business Analytics Research Lab, India
24 July,2018
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Content
SL no. Book Chapters Page no.
Part I
1 Deep analytics for technology innovation
management
Part II
2 Solar power electronics : Deep analytics
3 Electrical & hybrid vehicles : Deep Analytics
4 AI enabled smart grid : Deep Analytics
5 IIOT, SCADA & ICS
Part III
6 Cancer prevention mechanism : Deep learning
7Bio-medical instrumentation challenges
Part IV
8 Artificial rainfall, cloud physics & collaborative
resource sharing
9 Real-time Search for astronomical hazards
10. Conclusion
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Deep Analytics
for Technology Innovation Management
1. Introduction
Figure 1.1: Business Analytics for technology innovation projects
Let us highlight the organization of our work. The work starts with the problem of
technology innovation project management. It presents various models of business
analytics such as Deep analytics ‘7-S’ model, SWOT Analysis and Technology life
cycle analysis in terms of S-curve. Next, the business analytics framework is
decomposed following a decision tree like structure (Figure 1.1.)
Technology requirements engineering schema
o Scope
o System
Technology schema
o System
Computing schema
Networking schema
Business analytics
for technology innovation projects
Requirements
engineering
Technology
schema
Technology
management schema
ScopeStrategySecurity StructureSystemSkill,
Style,
support
Staff
Critical success
factors analysis
Value chain
analysis
Business Process
mapping
Computing schema
Networking schema
Data schema
Application schema
Application
integration
Project analytics
Financial
investment model
Organization
structure。